Performance¶

!pip install mlfinlab
!pip install yfinance
# See https://plotly.com/python/static-image-export/
!wget https://github.com/plotly/orca/releases/download/v1.2.1/orca-1.2.1-x86_64.AppImage -O /usr/local/bin/orca
!chmod +x /usr/local/bin/orca
!apt-get install xvfb libgtk2.0-0 libgconf-2-4

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import yfinance as yf
# "BB", "CVE", "NTAR", "HIVE", "N", "ACB", "APHA", "LSPD", "DSCI", "ERTH", "TTT", "AC", "HUT", "OCFT", "WIFI", "MVIS"
# DSCI NOT VIABLE THROUGH THIS PERIOD 2019-06-01

def enable_plotly_in_cell():
  import IPython
  from plotly.offline import init_notebook_mode
  display(IPython.core.display.HTML('''<script src="/static/components/requirejs/require.js"></script>'''))
  init_notebook_mode(connected=False)
enable_plotly_in_cell()

import pandas as pd 
import plotly.offline as py
import plotly.graph_objs as go
import plotly.io as pio
from mlfinlab.online_portfolio_selection.benchmarks import *

def generate_performance(stocks, start_date="2020-03-01", end_date="2020-05-30", title="Canadian Stocks"):
  stocks_string = " ".join(stocks)
  data = yf.download(stocks_string, start=start_date, end=end_date,
                    group_by="ticker")
  data = data.fillna(method='ffill')
  # Drop columns with no entries
  data = data.dropna(axis='columns', how='all')

  pio.renderers.default = "svg" # Toggle for GitHub rendering.
  nyse = pd.concat([data[ticker]["Close"] for ticker in stocks], axis=1)
  nyse.columns = stocks
  nyse_bah = BAH()
  nyse_bah.allocate(nyse)
  nyse_beststock = BestStock()
  nyse_beststock.allocate(nyse)
  nyse_crp = CRP()
  nyse_crp.allocate(nyse)
  nyse_bcrp = BCRP()
  nyse_bcrp.allocate(nyse)
  fig = go.Figure()
  idx = nyse_bah.portfolio_return.index
  fig.add_trace(go.Scatter(x=idx, y=nyse_beststock.portfolio_return['Returns'], name="Best Stock"))
  fig.add_trace(go.Scatter(x=idx, y=nyse_bah.portfolio_return['Returns'], name="Buy and Hold"))
  fig.add_trace(go.Scatter(x=idx, y=nyse_crp.portfolio_return['Returns'], name="CRP"))
  fig.add_trace(go.Scatter(x=idx, y=nyse_bcrp.portfolio_return['Returns'], name="BCRP"))
  fig.update_layout(title=title, xaxis_title='Date', yaxis_title='Relative Returns')
  fig.show()
  pio.write_image(fig, "gantt_suma.png", scale=3)


stocks_all = ["NEXCF", "ACB.TO", "BB.TO", "N.V", "APHA.TO", "TTT.CN", "HUT.TO", "HIVE.V", "AC.TO", "CVE.TO"]

# What I could have made if I all ined
generate_performance(stocks_all, start_date='2019-07-29', end_date='2020-05-30', title="What I could have made if I all ined ntar")

[*********************100%***********************]  10 of 10 completed

../../_images/Performance_06-01-2020_3_3.svg

# Potential performance before the crash
generate_performance(stocks_all, start_date='2019-09-29', end_date='2020-02-28', title="Performance Before Crash")

[*********************100%***********************]  10 of 10 completed

../../_images/Performance_06-01-2020_4_1.svg

# Potential performance before the crash
generate_performance(stocks_all, start_date='2020-02-27', end_date='2020-06-01', title="Performance After Crash")

[*********************100%***********************]  10 of 10 completed

../../_images/Performance_06-01-2020_5_1.svg

# Without top performer
# Potential performance before the crash
beta_stocks = ["ACB.TO", "BB.TO", "N.V", "APHA.TO", "TTT.CN", "HUT.TO", "HIVE.V", "AC.TO", "CVE.TO"]
generate_performance(beta_stocks, start_date='2019-09-29', end_date='2020-02-28', title="Performance Before Crash without top performer")

[*********************100%***********************]  9 of 9 completed

../../_images/Performance_06-01-2020_6_1.svg

# Potential performance before the crash
beta_stocks = ["ACB.TO", "BB.TO", "N.V", "APHA.TO", "TTT.CN", "HUT.TO", "HIVE.V", "AC.TO", "CVE.TO"]
generate_performance(beta_stocks, start_date='2020-02-27', end_date='2020-06-01', title="Performance After Crash")

[*********************100%***********************]  9 of 9 completed

../../_images/Performance_06-01-2020_7_1.svg

Investing Reference Book

Performance

Performance¶

Performance¶